{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"BERT_classification.ipynb","provenance":[],"collapsed_sections":["zIhXYygwcIC8","zAy1Kxz2za5L","P81N0jHCp94O","w5EeGuMEXvYV","OaaCjfTBclag","LVfqVH8BgaLi","3_ebMQaKBgX_","c2tPtP3w82Y5","AZoyzyD_9vLg"],"authorship_tag":"ABX9TyNl3g0SIBldqHW2shgpXWuD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"zIhXYygwcIC8","colab_type":"text"},"source":["### 导入库,定义一些全局常量"]},{"cell_type":"code","metadata":{"id":"XuKSOcoe6Byf","colab_type":"code","outputId":"f6c83399-e53a-4f6b-af2f-d23325fd8c9c","executionInfo":{"status":"ok","timestamp":1586235433363,"user_tz":-480,"elapsed":4305,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["import torch\n","torch.cuda.is_available()"],"execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"code","metadata":{"id":"74UW-W8jBzlv","colab_type":"code","outputId":"9f1f75af-e023-4b9c-a587-dcd64b9aab33","executionInfo":{"status":"ok","timestamp":1586235443264,"user_tz":-480,"elapsed":8675,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":683}},"source":["!pip install transformers"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting transformers\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a3/78/92cedda05552398352ed9784908b834ee32a0bd071a9b32de287327370b7/transformers-2.8.0-py3-none-any.whl (563kB)\n","\r\u001b[K     |▋                               | 10kB 12.4MB/s eta 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collected packages: tokenizers, sentencepiece, sacremoses, transformers\n","Successfully installed sacremoses-0.0.38 sentencepiece-0.1.85 tokenizers-0.5.2 transformers-2.8.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"qVFdEX0OB3EM","colab_type":"code","colab":{}},"source":["import numpy as np\n","import pandas as pd\n","import torch\n","import torch.nn as nn\n","from torch.nn import CrossEntropyLoss, MSELoss\n","import torch.nn.functional as F\n","\n","import torch.optim as optim\n","from torchtext.data import BucketIterator\n","\n","from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset\n","from torch.utils.data.distributed import DistributedSampler\n","from tqdm import tqdm, trange"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pdHa4VJACEWg","colab_type":"code","colab":{}},"source":["from transformers import (\n","    WEIGHTS_NAME,\n","    AdamW,\n","    get_linear_schedule_with_warmup,\n","    BertConfig,\n","    BertModel,\n","    BertPreTrainedModel,\n","    BertTokenizer,)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9I-ubcuGCGYn","colab_type":"code","colab":{}},"source":["from transformers import glue_convert_examples_to_features as convert_examples_to_features\n","from transformers import glue_output_modes as output_modes\n","from transformers import glue_processors as processors\n","from transformers.data.processors.utils import InputExample, DataProcessor"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xHA2hSQWZBlE","colab_type":"code","colab":{}},"source":["import logging\n","logger=logging.getLogger(__name__)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"iSDA0NkRCSu4","colab_type":"code","outputId":"174e01d9-db76-4ab8-c1de-68c506bccc51","executionInfo":{"status":"ok","timestamp":1586235484569,"user_tz":-480,"elapsed":24345,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":7,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_Ro00gFyeIe4","colab_type":"code","colab":{}},"source":["MODEL_CLASSES={\n","    \"bert\":(BertConfig,BertTokenizer),\n","}\n","\n","my_label_list=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n","MAX_SEQ_LENGTH=200\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zAy1Kxz2za5L","colab_type":"text"},"source":["### 这块可以暂时不要了"]},{"cell_type":"code","metadata":{"id":"fUUgBCrsB9mm","colab_type":"code","outputId":"3733cda4-1dcb-4325-8e71-c3974a751f69","executionInfo":{"status":"ok","timestamp":1585925159200,"user_tz":-480,"elapsed":4785,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":428}},"source":["train_raw = pd.read_csv('/content/drive/My Drive/Data/consumer_complaints.csv')\n","train_raw.head()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5,11) have mixed types.Specify dtype option on import or set low_memory=False.\n","  interactivity=interactivity, compiler=compiler, result=result)\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>date_received</th>\n","      <th>product</th>\n","      <th>sub_product</th>\n","      <th>issue</th>\n","      <th>sub_issue</th>\n","      <th>consumer_complaint_narrative</th>\n","      <th>company_public_response</th>\n","      <th>company</th>\n","      <th>state</th>\n","      <th>zipcode</th>\n","      <th>tags</th>\n","      <th>consumer_consent_provided</th>\n","      <th>submitted_via</th>\n","      <th>date_sent_to_company</th>\n","      <th>company_response_to_consumer</th>\n","      <th>timely_response</th>\n","      <th>consumer_disputed?</th>\n","      <th>complaint_id</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>08/30/2013</td>\n","      <td>Mortgage</td>\n","      <td>Other mortgage</td>\n","      <td>Loan modification,collection,foreclosure</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>U.S. Bancorp</td>\n","      <td>CA</td>\n","      <td>95993</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Referral</td>\n","      <td>09/03/2013</td>\n","      <td>Closed with explanation</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>511074</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>08/30/2013</td>\n","      <td>Mortgage</td>\n","      <td>Other mortgage</td>\n","      <td>Loan servicing, payments, escrow account</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Wells Fargo &amp; Company</td>\n","      <td>CA</td>\n","      <td>91104</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Referral</td>\n","      <td>09/03/2013</td>\n","      <td>Closed with explanation</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>511080</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>08/30/2013</td>\n","      <td>Credit reporting</td>\n","      <td>NaN</td>\n","      <td>Incorrect information on credit report</td>\n","      <td>Account status</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Wells Fargo &amp; Company</td>\n","      <td>NY</td>\n","      <td>11764</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Postal mail</td>\n","      <td>09/18/2013</td>\n","      <td>Closed with explanation</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>510473</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>08/30/2013</td>\n","      <td>Student loan</td>\n","      <td>Non-federal student loan</td>\n","      <td>Repaying your loan</td>\n","      <td>Repaying your loan</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Navient Solutions, Inc.</td>\n","      <td>MD</td>\n","      <td>21402</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Email</td>\n","      <td>08/30/2013</td>\n","      <td>Closed with explanation</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>510326</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>08/30/2013</td>\n","      <td>Debt collection</td>\n","      <td>Credit card</td>\n","      <td>False statements or representation</td>\n","      <td>Attempted to collect wrong amount</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Resurgent Capital Services L.P.</td>\n","      <td>GA</td>\n","      <td>30106</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Web</td>\n","      <td>08/30/2013</td>\n","      <td>Closed with explanation</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>511067</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  date_received           product  ... consumer_disputed? complaint_id\n","0    08/30/2013          Mortgage  ...                Yes       511074\n","1    08/30/2013          Mortgage  ...                Yes       511080\n","2    08/30/2013  Credit reporting  ...                 No       510473\n","3    08/30/2013      Student loan  ...                Yes       510326\n","4    08/30/2013   Debt collection  ...                Yes       511067\n","\n","[5 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"Z1b_tJA9DUjy","colab_type":"code","outputId":"f5c307ca-dbd7-4175-fdf6-cc485236315a","executionInfo":{"status":"ok","timestamp":1585900783373,"user_tz":-480,"elapsed":597,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_raw.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(555957, 18)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"zYyzLix7DY3b","colab_type":"code","outputId":"c22e9f56-9917-4287-add6-c214e4d22fe3","executionInfo":{"status":"ok","timestamp":1585900798117,"user_tz":-480,"elapsed":1450,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_raw = train_raw[train_raw.consumer_complaint_narrative.notnull()]\n","train_raw.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(66806, 18)"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"t-yxxTEBDc9F","colab_type":"code","outputId":"0e54126e-c440-43b5-ae52-8e379589fc59","executionInfo":{"status":"ok","timestamp":1585900815116,"user_tz":-480,"elapsed":1881,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":282}},"source":["train_raw.consumer_complaint_narrative.apply(lambda x: len(x.split())).plot(kind='hist')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fe8d62f87b8>"]},"metadata":{"tags":[]},"execution_count":11},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZEAAAD4CAYAAAAtrdtxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktSZISJJ6swQkSR1NrQQSbI+yUNJbu+rHZlka5K7288FrZ4kl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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Oxf2eQSDDgy-","colab_type":"code","colab":{}},"source":["train_raw['len_txt'] =train_raw.consumer_complaint_narrative.apply(lambda x: len(x.split()))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"lpkQJJEkDqyu","colab_type":"code","outputId":"3043f6ff-5731-4824-99ed-f2443c66cc68","executionInfo":{"status":"ok","timestamp":1585900861675,"user_tz":-480,"elapsed":1350,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_raw.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(66806, 19)"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"npjQ2Bi8DuQA","colab_type":"code","outputId":"d5c6b5be-d016-469e-a487-8c50abf6a4a4","executionInfo":{"status":"ok","timestamp":1585900877266,"user_tz":-480,"elapsed":1405,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_raw = train_raw[train_raw.len_txt >249]\n","train_raw.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(17142, 19)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"K7i3IUhFDyRc","colab_type":"code","outputId":"0d4c1f9f-4a76-4f35-bf32-3156ef2c4836","executionInfo":{"status":"ok","timestamp":1585900891488,"user_tz":-480,"elapsed":515,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train_raw = train_raw[['consumer_complaint_narrative', 'product']]\n","train_raw.reset_index(inplace=True, drop=True)\n","train_raw.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>consumer_complaint_narrative</th>\n","      <th>product</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In XX/XX/XXXX my wages that I earned at my job...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>XXXX was submitted XX/XX/XXXX. At the time I s...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I spoke to XXXX of green tree representatives ...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>i opened XXXX Bank of America credit cards 15-...</td>\n","      <td>Credit card</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I applied for a loan with XXXX XXXX and had pu...</td>\n","      <td>Consumer Loan</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                        consumer_complaint_narrative        product\n","0  In XX/XX/XXXX my wages that I earned at my job...       Mortgage\n","1  XXXX was submitted XX/XX/XXXX. At the time I s...       Mortgage\n","2  I spoke to XXXX of green tree representatives ...       Mortgage\n","3  i opened XXXX Bank of America credit cards 15-...    Credit card\n","4  I applied for a loan with XXXX XXXX and had pu...  Consumer Loan"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"2itTFz6aES1z","colab_type":"code","outputId":"0f8cb931-e44c-4726-84dd-c76f3aa13dd0","executionInfo":{"status":"ok","timestamp":1585901026618,"user_tz":-480,"elapsed":1078,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train_raw.at[train_raw['product'] == 'Credit reporting', 'product'] = 'Credit reporting, credit repair services, or other personal consumer reports'\n","train_raw.at[train_raw['product'] == 'Credit card', 'product'] = 'Credit card or prepaid card'\n","train_raw.at[train_raw['product'] == 'Prepaid card', 'product'] = 'Credit card or prepaid card'\n","train_raw.at[train_raw['product'] == 'Payday loan', 'product'] = 'Payday loan, title loan, or personal loan'\n","train_raw.at[train_raw['product'] == 'Virtual currency', 'product'] = 'Money transfer, virtual currency, or money service'\n","train_raw.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>consumer_complaint_narrative</th>\n","      <th>product</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In XX/XX/XXXX my wages that I earned at my job...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>XXXX was submitted XX/XX/XXXX. At the time I s...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I spoke to XXXX of green tree representatives ...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>i opened XXXX Bank of America credit cards 15-...</td>\n","      <td>Credit card or prepaid card</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I applied for a loan with XXXX XXXX and had pu...</td>\n","      <td>Consumer Loan</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                        consumer_complaint_narrative                      product\n","0  In XX/XX/XXXX my wages that I earned at my job...                     Mortgage\n","1  XXXX was submitted XX/XX/XXXX. At the time I s...                     Mortgage\n","2  I spoke to XXXX of green tree representatives ...                     Mortgage\n","3  i opened XXXX Bank of America credit cards 15-...  Credit card or prepaid card\n","4  I applied for a loan with XXXX XXXX and had pu...                Consumer Loan"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"gMzTIizWEZYs","colab_type":"code","outputId":"ad304f6e-7e3c-4053-c617-928b30cb14a8","executionInfo":{"status":"error","timestamp":1585977992388,"user_tz":-480,"elapsed":1094,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":287}},"source":["train_raw=train_raw.rename(columns = {'consumer_complaint_narrative':'text', 'product':'label'})\n","train_raw.head()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-9-30084b039307>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_raw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_raw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'consumer_complaint_narrative'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'text'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'product'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtrain_raw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'train_raw' is not defined","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: Current TensorFlow version is 2.2.0-rc2. To use TF 1.x instead,\nrestart your runtime (Ctrl+M .) and run \"%tensorflow_version 1.x\" before\nyou run \"import tensorflow\".\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"]}]},{"cell_type":"code","metadata":{"id":"Hm-WyNXAEd1V","colab_type":"code","colab":{}},"source":["train_raw.to_csv(\"/content/drive/My Drive/Data/consumer_complaint_narrative.csv\",index=0)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"P81N0jHCp94O","colab_type":"text"},"source":["### 读入csv,初步处理数据, 处理完获得 val(3429, 2), train(13713, 2)"]},{"cell_type":"code","metadata":{"id":"09RtcAiSgiwA","colab_type":"code","outputId":"ed58818e-e6f1-44f3-f5d4-1b01458e9e7c","executionInfo":{"status":"ok","timestamp":1586235495206,"user_tz":-480,"elapsed":1592,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train_raw = pd.read_csv('/content/drive/My Drive/Data/consumer_complaint_narrative.csv')\n","train_raw.head()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In XX/XX/XXXX my wages that I earned at my job...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>XXXX was submitted XX/XX/XXXX. At the time I s...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I spoke to XXXX of green tree representatives ...</td>\n","      <td>Mortgage</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>i opened XXXX Bank of America credit cards 15-...</td>\n","      <td>Credit card or prepaid card</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I applied for a loan with XXXX XXXX and had pu...</td>\n","      <td>Consumer Loan</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text                        label\n","0  In XX/XX/XXXX my wages that I earned at my job...                     Mortgage\n","1  XXXX was submitted XX/XX/XXXX. At the time I s...                     Mortgage\n","2  I spoke to XXXX of green tree representatives ...                     Mortgage\n","3  i opened XXXX Bank of America credit cards 15-...  Credit card or prepaid card\n","4  I applied for a loan with XXXX XXXX and had pu...                Consumer Loan"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"ET2OCt0yGTfY","colab_type":"code","outputId":"9ab8894b-1ad8-462e-9e39-dbbb76f4df3c","executionInfo":{"status":"ok","timestamp":1586235497121,"user_tz":-480,"elapsed":529,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["from sklearn.preprocessing import LabelEncoder\n","\n","LE = LabelEncoder()\n","train_raw['label'] = LE.fit_transform(train_raw['label'])\n","train_raw.head()"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>In XX/XX/XXXX my wages that I earned at my job...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>XXXX was submitted XX/XX/XXXX. At the time I s...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>I spoke to XXXX of green tree representatives ...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>i opened XXXX Bank of America credit cards 15-...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I applied for a loan with XXXX XXXX and had pu...</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  label\n","0  In XX/XX/XXXX my wages that I earned at my job...      6\n","1  XXXX was submitted XX/XX/XXXX. At the time I s...      6\n","2  I spoke to XXXX of green tree representatives ...      6\n","3  i opened XXXX Bank of America credit cards 15-...      2\n","4  I applied for a loan with XXXX XXXX and had pu...      1"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"3kzO1xZ7G0dt","colab_type":"code","outputId":"4d13f77a-8ac3-4fb5-fba5-1524b9239d16","executionInfo":{"status":"ok","timestamp":1586235500128,"user_tz":-480,"elapsed":1180,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train = train_raw.copy()\n","train = train.reindex(np.random.permutation(train.index))\n","train.head()"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>9042</th>\n","      <td>I became a victim from XXXX/broker \" s name [ ...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5289</th>\n","      <td>XXXX XXXX XXXX XXXX listed collection accounts...</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3491</th>\n","      <td>This complaint is about SETERUS Mortgage Servi...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>8989</th>\n","      <td>I WISH TO FILE A COMPLAINT AGAINST XXXX. DBA N...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>9620</th>\n","      <td>I have a mortgage with Bank of America. I curr...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                   text  label\n","9042  I became a victim from XXXX/broker \" s name [ ...      6\n","5289  XXXX XXXX XXXX XXXX listed collection accounts...      3\n","3491  This complaint is about SETERUS Mortgage Servi...      6\n","8989  I WISH TO FILE A COMPLAINT AGAINST XXXX. DBA N...      6\n","9620  I have a mortgage with Bank of America. I curr...      6"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"dDRl_4xRI7ur","colab_type":"code","colab":{}},"source":["import re\n","def clean_txt(text):\n","  text = re.sub(\"'\", \"\",text)\n","  text=re.sub(\"(\\\\W)+\",\" \",text)    \n","  return text"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VFOxoUCDJ5wJ","colab_type":"code","outputId":"b14c500c-0ce0-45b3-c725-fa2983f92eb9","executionInfo":{"status":"ok","timestamp":1586235509178,"user_tz":-480,"elapsed":4819,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train['text']  = train.text.apply(clean_txt)\n","train.head()"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>9042</th>\n","      <td>I became a victim from XXXX broker s name XXXX...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>5289</th>\n","      <td>XXXX XXXX XXXX XXXX listed collection accounts...</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>3491</th>\n","      <td>This complaint is about SETERUS Mortgage Servi...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>8989</th>\n","      <td>I WISH TO FILE A COMPLAINT AGAINST XXXX DBA NE...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>9620</th>\n","      <td>I have a mortgage with Bank of America I curre...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                   text  label\n","9042  I became a victim from XXXX broker s name XXXX...      6\n","5289  XXXX XXXX XXXX XXXX listed collection accounts...      3\n","3491  This complaint is about SETERUS Mortgage Servi...      6\n","8989  I WISH TO FILE A COMPLAINT AGAINST XXXX DBA NE...      6\n","9620  I have a mortgage with Bank of America I curre...      6"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"pJ7LmZ9YRYeF","colab_type":"code","outputId":"8e44dbf2-0a00-481f-a513-9809c25b795a","executionInfo":{"status":"ok","timestamp":1586235510847,"user_tz":-480,"elapsed":1180,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["from sklearn.model_selection import train_test_split\n","train, val = train_test_split(train, test_size=0.2, random_state=35)\n","train.head()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>868</th>\n","      <td>I have a Regular checking account with Bank of...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>12514</th>\n","      <td>We have XXXX mortgages with Greentree Servicin...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>918</th>\n","      <td>Extremely poor customer service tracking monit...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10801</th>\n","      <td>My issue deals with my mortgage company and fl...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>15022</th>\n","      <td>I applied for a loan modification to save my h...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                    text  label\n","868    I have a Regular checking account with Bank of...      0\n","12514  We have XXXX mortgages with Greentree Servicin...      4\n","918    Extremely poor customer service tracking monit...      1\n","10801  My issue deals with my mortgage company and fl...      6\n","15022  I applied for a loan modification to save my h...      6"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"E9LM6NEwRg7E","colab_type":"code","outputId":"03e866a8-6617-4a55-a7b5-15ef597e5e05","executionInfo":{"status":"ok","timestamp":1586235512806,"user_tz":-480,"elapsed":564,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":111}},"source":["train.reset_index(drop=True, inplace=True)\n","train.head(2)"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I have a Regular checking account with Bank of...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>We have XXXX mortgages with Greentree Servicin...</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  label\n","0  I have a Regular checking account with Bank of...      0\n","1  We have XXXX mortgages with Greentree Servicin...      4"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"fIDgE55kRiLO","colab_type":"code","outputId":"1d529b4c-7ed7-4026-a6f7-4fba2dda4cb9","executionInfo":{"status":"ok","timestamp":1586235515559,"user_tz":-480,"elapsed":1191,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":111}},"source":["val.reset_index(drop=True, inplace=True)\n","val.head(2)"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I received a credit card statement from Capita...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>I am executor of my father s estate My father ...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  label\n","0  I received a credit card statement from Capita...      2\n","1  I am executor of my father s estate My father ...      6"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"LJ3JKI2WRlsv","colab_type":"code","outputId":"ad0ffe40-72a7-4da8-d30d-279902905c7f","executionInfo":{"status":"ok","timestamp":1586235517827,"user_tz":-480,"elapsed":1259,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["val.shape, train.shape"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((3429, 2), (13713, 2))"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"w5EeGuMEXvYV","colab_type":"text"},"source":["### 按照每200大小切割文本,处理完获得 train_df(31698,2), val_df(7927,2)"]},{"cell_type":"code","metadata":{"id":"_qS-jaUWXysV","colab_type":"code","colab":{}},"source":["def get_split(text):\n","    l_total = []\n","    l_parcial = []\n","    if len(text.split())//150 >0:\n","      n = len(text.split())//150\n","    else: \n","      n = 1\n","    for w in range(n):\n","      if w == 0:\n","        l_parcial = text.split()[:200]\n","        l_total.append(\" \".join(l_parcial))\n","      else:\n","        l_parcial = text.split()[w*150:w*150 + 200]\n","        l_total.append(\" \".join(l_parcial))\n","    return l_total"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"d4-uWGn0YRp9","colab_type":"code","outputId":"d793c86e-af27-430c-f7da-e6c57c9e55e3","executionInfo":{"status":"ok","timestamp":1586235529349,"user_tz":-480,"elapsed":2030,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train['text_split'] = train['text'].apply(get_split)\n","train.head()"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","      <th>text_split</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I have a Regular checking account with Bank of...</td>\n","      <td>0</td>\n","      <td>[I have a Regular checking account with Bank o...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>We have XXXX mortgages with Greentree Servicin...</td>\n","      <td>4</td>\n","      <td>[We have XXXX mortgages with Greentree Servici...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Extremely poor customer service tracking monit...</td>\n","      <td>1</td>\n","      <td>[Extremely poor customer service tracking moni...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>My issue deals with my mortgage company and fl...</td>\n","      <td>6</td>\n","      <td>[My issue deals with my mortgage company and f...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>I applied for a loan modification to save my h...</td>\n","      <td>6</td>\n","      <td>[I applied for a loan modification to save my ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  ...                                         text_split\n","0  I have a Regular checking account with Bank of...  ...  [I have a Regular checking account with Bank o...\n","1  We have XXXX mortgages with Greentree Servicin...  ...  [We have XXXX mortgages with Greentree Servici...\n","2  Extremely poor customer service tracking monit...  ...  [Extremely poor customer service tracking moni...\n","3  My issue deals with my mortgage company and fl...  ...  [My issue deals with my mortgage company and f...\n","4  I applied for a loan modification to save my h...  ...  [I applied for a loan modification to save my ...\n","\n","[5 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"CTVciiBhZE99","colab_type":"code","outputId":"dd77a269-9741-471b-9b81-8aacc2bd005a","executionInfo":{"status":"ok","timestamp":1586235531584,"user_tz":-480,"elapsed":1063,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["val['text_split'] = val['text'].apply(get_split)\n","val.head()"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","      <th>text_split</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I received a credit card statement from Capita...</td>\n","      <td>2</td>\n","      <td>[I received a credit card statement from Capit...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>I am executor of my father s estate My father ...</td>\n","      <td>6</td>\n","      <td>[I am executor of my father s estate My father...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>In XX XX XXXX I financed a Flat Screen TV thro...</td>\n","      <td>1</td>\n","      <td>[In XX XX XXXX I financed a Flat Screen TV thr...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1 When loan documents were signed XX XX XXXX B...</td>\n","      <td>6</td>\n","      <td>[1 When loan documents were signed XX XX XXXX ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>OCWEN is the servicing bank has held homeowner...</td>\n","      <td>6</td>\n","      <td>[OCWEN is the servicing bank has held homeowne...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  ...                                         text_split\n","0  I received a credit card statement from Capita...  ...  [I received a credit card statement from Capit...\n","1  I am executor of my father s estate My father ...  ...  [I am executor of my father s estate My father...\n","2  In XX XX XXXX I financed a Flat Screen TV thro...  ...  [In XX XX XXXX I financed a Flat Screen TV thr...\n","3  1 When loan documents were signed XX XX XXXX B...  ...  [1 When loan documents were signed XX XX XXXX ...\n","4  OCWEN is the servicing bank has held homeowner...  ...  [OCWEN is the servicing bank has held homeowne...\n","\n","[5 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"1lpq601QZKnt","colab_type":"code","outputId":"dacf3cd1-5110-4a76-da03-571bc9ae850b","executionInfo":{"status":"ok","timestamp":1586235535739,"user_tz":-480,"elapsed":2756,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["train_l = []  # 分割好的文本\n","label_l = []  # 每段文本的label\n","index_l =[]   # 该段文本属于未分割前的哪个文本. 比如是3, 那就表明此段文本是属于未分割前的第4条数据\n","for idx,row in train.iterrows():\n","  for l in row['text_split']:\n","    train_l.append(l)\n","    label_l.append(row['label'])\n","    index_l.append(idx)\n","len(train_l), len(label_l), len(index_l)"],"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(31600, 31600, 31600)"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"aB_6ZC6NZSoA","colab_type":"code","outputId":"18a9f878-3d42-4f8e-82dc-7bee88539ff4","executionInfo":{"status":"ok","timestamp":1586235538524,"user_tz":-480,"elapsed":1009,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["val_l = []\n","val_label_l = []\n","val_index_l = []\n","for idx,row in val.iterrows():\n","  for l in row['text_split']:\n","    val_l.append(l)\n","    val_label_l.append(row['label'])\n","    val_index_l.append(idx)\n","len(val_l), len(val_label_l), len(val_index_l)"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8025, 8025, 8025)"]},"metadata":{"tags":[]},"execution_count":22}]},{"cell_type":"code","metadata":{"id":"yt6_LomQZYA8","colab_type":"code","outputId":"f3f8407c-1a63-44e8-8515-82305fae679e","executionInfo":{"status":"ok","timestamp":1586235541333,"user_tz":-480,"elapsed":1312,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train_df = pd.DataFrame({'text':train_l, 'label':label_l})\n","train_df.head()"],"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I have a Regular checking account with Bank of...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>We have XXXX mortgages with Greentree Servicin...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Writ of Possession which is required by Vermon...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>XXXX XXXX was the officer who returned our cal...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>you know the plaintiff s attorney finally resp...</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  label\n","0  I have a Regular checking account with Bank of...      0\n","1  We have XXXX mortgages with Greentree Servicin...      4\n","2  Writ of Possession which is required by Vermon...      4\n","3  XXXX XXXX was the officer who returned our cal...      4\n","4  you know the plaintiff s attorney finally resp...      4"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"id":"RqiMRI3XZjo1","colab_type":"code","outputId":"7bbd5774-19ba-46a0-fec2-837d3ff46edc","executionInfo":{"status":"ok","timestamp":1586235543966,"user_tz":-480,"elapsed":1261,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["val_df = pd.DataFrame({'text':val_l, 'label':val_label_l})\n","val_df.head()"],"execution_count":24,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I received a credit card statement from Capita...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>I am executor of my father s estate My father ...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>In XX XX XXXX I financed a Flat Screen TV thro...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>statement which I rarely did since i would jus...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1 When loan documents were signed XX XX XXXX B...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                text  label\n","0  I received a credit card statement from Capita...      2\n","1  I am executor of my father s estate My father ...      6\n","2  In XX XX XXXX I financed a Flat Screen TV thro...      1\n","3  statement which I rarely did since i would jus...      1\n","4  1 When loan documents were signed XX XX XXXX B...      6"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"TIeydiHARoqC","colab_type":"code","colab":{}},"source":["train_InputExamples = train_df.apply(lambda x: InputExample(guid=None,\n","                                                                   text_a = x['text'], \n","                                                                   text_b = None, \n","                                                                   label = x['label']), axis = 1)\n","\n","val_InputExamples = val_df.apply(lambda x: InputExample(guid=None, \n","                                                                   text_a = x['text'], \n","                                                                   text_b = None, \n","                                                                   label = x['label']), axis = 1)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"OaaCjfTBclag","colab_type":"text"},"source":["### 定义模型"]},{"cell_type":"code","metadata":{"id":"W8vhUnMsILet","colab_type":"code","colab":{}},"source":["class BertForClassification(BertPreTrainedModel):\n","    def __init__(self, config):\n","        super().__init__(config)\n","        self.num_labels = 10\n","\n","        self.bert = BertModel(config)\n","        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n","        self.classifier = nn.Linear(config.hidden_size, self.num_labels)\n","\n","        self.init_weights()\n","\n","    def forward(\n","        self,\n","        input_ids=None,  #输入的id,模型会帮你把id转成embedding\n","        attention_mask=None,   #attention里的mask\n","        token_type_ids=None,    # [CLS]A[SEP]B[SEP] 就这个A还是B, 有的话就全1, 没有就0\n","        position_ids=None,     # 位置id\n","        head_mask=None,       # 哪个head需要被mask掉\n","        inputs_embeds=None,   # 可以选择不输入id,直接输入embedding\n","        labels=None,          # 做分类时需要的label\n","    ):\n","\n","        outputs = self.bert(\n","            input_ids,\n","            attention_mask=attention_mask,\n","            token_type_ids=token_type_ids,\n","            position_ids=position_ids,\n","            head_mask=head_mask,\n","            inputs_embeds=inputs_embeds,\n","        )\n","\n","        sequence_output, pooled_output=outputs[:2]\n","\n","        pooled_output = self.dropout(pooled_output)\n","        logits = self.classifier(pooled_output)\n","\n","        \n","        outputs = (logits, pooled_output, sequence_output,)\n","\n","        if labels is not None:\n","            \n","            if self.num_labels == 1:\n","                #  We are doing regression\n","                loss_fct = MSELoss()\n","                loss = loss_fct(logits.view(-1), labels.view(-1))\n","            else:\n","                loss_fct = CrossEntropyLoss()\n","                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n","            outputs = (loss,) + outputs\n","\n","        \n","        return outputs  # loss, logits, pooled_output, sequence_output\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"LVfqVH8BgaLi","colab_type":"text"},"source":["### 载入预训练模型, 准备训练数据, 训练, 保存模型"]},{"cell_type":"code","metadata":{"id":"YVYmMuKBLrhh","colab_type":"code","outputId":"aece59e8-71ee-4c22-c648-e89e8d07a4ea","executionInfo":{"status":"ok","timestamp":1586007564445,"user_tz":-480,"elapsed":5060,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# 1.载入预训练模型\n","args={\"model_name_or_path\": \"/content/drive/My Drive/english_wwm_pytorch/\",\n","    \"config_name\": \"/content/drive/My Drive/english_wwm_pytorch/\",\n","    \"tokenizer_name\": \"/content/drive/My Drive/english_wwm_pytorch/\",\n","      }\n","\n","config_class, tokenizer_class = MODEL_CLASSES[\"bert\"]\n","model_class=BertForClassification\n","\n","\n","config = config_class.from_pretrained(\n","    args[\"config_name\"],\n","    finetuning_task=\"\", \n","    cache_dir=None,\n",")\n","tokenizer = tokenizer_class.from_pretrained(\n","    args[\"tokenizer_name\"],\n","    do_lower_case=True,\n","    cache_dir=None,\n",")\n","model = model_class.from_pretrained(\n","    args[\"model_name_or_path\"],\n","    from_tf=bool(\".ckpt\" in args[\"model_name_or_path\"]),\n","    config=config,\n","    cache_dir=None,\n",")\n","\n","\n","model.to(\"cuda\")"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BertForClassification(\n","  (bert): BertModel(\n","    (embeddings): BertEmbeddings(\n","      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (token_type_embeddings): Embedding(2, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (encoder): BertEncoder(\n","      (layer): ModuleList(\n","        (0): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (1): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (2): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (3): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (4): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (5): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (6): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (7): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (8): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (9): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (10): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (11): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (pooler): BertPooler(\n","      (dense): Linear(in_features=768, out_features=768, bias=True)\n","      (activation): Tanh()\n","    )\n","  )\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (classifier): Linear(in_features=768, out_features=10, bias=True)\n",")"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"q3sYLMfqLnWC","colab_type":"code","colab":{}},"source":["# 2.准备训练数据\n","train_features = convert_examples_to_features(train_InputExamples,\n","                                              tokenizer, \n","                                              label_list=my_label_list, \n","                                              output_mode=\"classification\", \n","                                              max_length=MAX_SEQ_LENGTH )"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yhi-R_VQ1pxp","colab_type":"code","colab":{}},"source":["# 看下长啥样\n","print(\"Sentence-1: \", train_InputExamples.iloc[0].text_a)\n","print(\"-\"*30)\n","print(\"Tokens : \", tokenizer.tokenize(train_InputExamples.iloc[0].text_a))\n","print(\"-\"*30)\n","print(\"Input IDs : \", train_features[0].input_ids)\n","print(\"-\"*30)\n","print(\"Input Masks : \", train_features[0].attention_mask)\n","print(\"-\"*30)\n","print(\"Token type IDS : \", train_features[0].token_type_ids)\n","print(\"-\"*30)\n","print(\"Label : \", train_features[0].label)\n","print(\"-\"*30)\n","print(\"-\"*30)\n","print(\"Sentence-2: \", train_InputExamples.iloc[1].text_a)\n","print(\"-\"*30)\n","print(\"Tokens : \", tokenizer.tokenize(train_InputExamples.iloc[1].text_a))\n","print(\"-\"*30)\n","print(\"Input IDs : \", train_features[1].input_ids)\n","print(\"-\"*30)\n","print(\"Input Masks : \", train_features[1].attention_mask)\n","print(\"-\"*30)\n","print(\"Token type IDS : \", train_features[1].token_type_ids)\n","print(\"-\"*30)\n","print(\"Label : \", train_features[1].label)\n","print(\"-\"*30)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DQP5rPHMZRPN","colab_type":"code","colab":{}},"source":["input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)\n","attention_mask = torch.tensor([f.attention_mask for f in train_features], dtype=torch.long)\n","token_type_ids = torch.tensor([f.token_type_ids for f in train_features], dtype=torch.long)\n","the_labels = torch.tensor([f.label for f in train_features], dtype=torch.long)\n","\n","# 这里一步每个东西都是要传到BERT模型的forward里面的, 要传哪些自己准备好\n","dataset = TensorDataset(input_ids, attention_mask, token_type_ids, the_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"72XJ5kNKUong","colab_type":"code","colab":{}},"source":["# 3.定义训练函数\n","def train(train_dataset,model,tokenizer):\n","    no_decay=[\"bias\",\"LayerNorm.weight\"]\n","    optimizer_grouped_parameters=[\n","        {\n","            \"params\":[p for n,p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n","            \"weight_decay\":0.0,\n","\n","        },\n","        {\n","            \"params\": [p for n,p in model.named_parameters() if any(nd in n for nd in no_decay)],\n","            \"weight_decay\":0.0\n","        },\n","    ]\n","\n","    \n","    t_total=len(train_dataset)// 5\n","    optimizer=AdamW(optimizer_grouped_parameters,lr=2e-5,eps=1e-8)\n","    # bert里的小技巧, bert里的learning rate是不断变化的,先往上升,再往下降,这个scheduler就是用来设置这个\n","    scheduler=get_linear_schedule_with_warmup(\n","        optimizer,num_warmup_steps=0,num_training_steps=t_total\n","        )\n","    \n","    \n","    \n","    # *********************\n","    logger.info(\"*****Running training*****\")\n","    logger.info(\"  Num examples = %d\", len(train_dataset))\n","    logger.info(\"  Num Epochs = %d\", 5)\n","\n","\n","    epochs_trained=0\n","    global_step=0\n","    steps_trained_in_current_epoch=0\n","\n","    tr_loss,logging_loss=0.0,0.0\n","    model.zero_grad()\n","    train_iterator=trange(epochs_trained,5,desc=\"Epoch\",disable=False)\n","\n","\n","    for k in train_iterator: #共5个epoch\n","    \n","        #随机打包\n","        train_sampler=RandomSampler(train_dataset)\n","        train_dataloader=DataLoader(train_dataset,sampler=train_sampler,batch_size=16)\n","        epoch_iterator=tqdm(train_dataloader,desc=\"Iteration\",disable=False)\n","\n","        for step,batch in enumerate(epoch_iterator): \n","            if steps_trained_in_current_epoch>0:\n","                steps_traned_in_current_epoch-=1\n","                continue\n","\n","            model.train()\n","            batch=tuple(t.to(\"cuda\") for t in batch)\n","            \n","            # 每个batch里是 input_ids, attention_mask, token_type_ids, the_labels\n","            # 所以传入模型时,每个参数位置对应好放进去.\n","            inputs={\"input_ids\": batch[0],\"attention_mask\": batch[1],\"token_type_ids\": batch[2], \"labels\": batch[3]}\n","   \n","            outputs = model(**inputs)\n","            loss=outputs[0]\n"," \n","            loss.backward()\n","\n","            tr_loss+=loss.item()\n","            if (step+1)%1==0:\n","                torch.nn.utils.clip_grad_norm_(model.parameters(),1.0)\n","\n","                optimizer.step()\n","                scheduler.step()\n","                model.zero_grad()\n","                global_step+=1\n","\n","        logger.info(\"average loss:\" +str(tr_loss/global_step))\n","\n","\n","    return global_step,tr_loss/global_step"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sLhbXtMbYQsA","colab_type":"code","outputId":"09dc7fb7-a063-4d2b-b43d-21ba991c9ea2","executionInfo":{"status":"ok","timestamp":1586014646002,"user_tz":-480,"elapsed":6905662,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# 4.训练\n","train(dataset,model,tokenizer)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\u001b[1;30;43m流式输出内容被截断，只能显示最后 5000 行内容。\u001b[0m\n","Iteration:  49%|████▊     | 968/1987 [11:13<11:48,  1.44it/s]\u001b[A\n","Iteration:  49%|████▉     | 969/1987 [11:13<11:46,  1.44it/s]\u001b[A\n","Iteration:  49%|████▉     | 970/1987 [11:14<11:45,  1.44it/s]\u001b[A\n","Iteration:  49%|████▉     | 971/1987 [11:15<11:44,  1.44it/s]\u001b[A\n","Iteration:  49%|████▉    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评估"]},{"cell_type":"code","metadata":{"id":"kyhCQqQH9oDO","colab_type":"code","outputId":"3b87979d-9206-4028-c56e-66fdcddd4fb6","executionInfo":{"status":"ok","timestamp":1586067745069,"user_tz":-480,"elapsed":28538,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# 1. 载入训练好的模型\n","args_eval={\"model_name_or_path\": \"/content/drive/My Drive/classification_models_1/\",\n","    \"config_name\": \"/content/drive/My Drive/classification_models_1/\",\n","    \"tokenizer_name\": \"/content/drive/My Drive/classification_models_1/\",\n","      }\n","\n","config_class, tokenizer_class = MODEL_CLASSES[\"bert\"]\n","model_class=BertForClassification\n","\n","\n","config = config_class.from_pretrained(\n","    args_eval[\"config_name\"],\n","    finetuning_task=\"\", \n","    cache_dir=None,\n",")\n","tokenizer = tokenizer_class.from_pretrained(\n","    args_eval[\"tokenizer_name\"],\n","    do_lower_case=True,\n","    cache_dir=None,\n",")\n","model = model_class.from_pretrained(\n","    args_eval[\"model_name_or_path\"],\n","    from_tf=bool(\".ckpt\" in args_eval[\"model_name_or_path\"]),\n","    config=config,\n","    cache_dir=None,\n",")\n","\n","\n","model.to(\"cuda\")"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BertForClassification(\n","  (bert): BertModel(\n","    (embeddings): BertEmbeddings(\n","      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (token_type_embeddings): Embedding(2, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (encoder): BertEncoder(\n","      (layer): ModuleList(\n","        (0): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (1): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (2): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (3): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (4): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (5): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (6): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (7): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (8): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (9): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (10): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (11): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (pooler): BertPooler(\n","      (dense): Linear(in_features=768, out_features=768, bias=True)\n","      (activation): Tanh()\n","    )\n","  )\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (classifier): Linear(in_features=768, out_features=10, bias=True)\n",")"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"id":"_cDoN65254Kr","colab_type":"code","colab":{}},"source":["# 2.定义评估函数函数\n","from sklearn.metrics import f1_score\n","\n","def simple_accuracy(preds, labels):\n","    return (preds == labels).mean()\n","def acc_and_f1(preds, labels):\n","    acc = simple_accuracy(preds, labels)\n","    f1 = f1_score(y_true=labels, y_pred=preds)\n","    return {\n","        \"acc\": acc,\n","        \"f1\": f1,\n","        \"acc_and_f1\": (acc + f1) / 2,\n","    }\n","\n","\n","\n","\n","\n","def evaluate(model, tokenizer, eval_dataset):\n","\n","\n","    logger.info(\"***** Running evaluation  *****\")\n","    logger.info(\"  Num examples = %d\", len(eval_dataset))\n","    logger.info(\"  Batch size = %d\", 16)\n","    eval_loss = 0.0\n","    nb_eval_steps = 0\n","    preds = None\n","    out_label_ids = None\n","\n","    eval_sampler =RandomSampler(eval_dataset)\n","    eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=16)\n","\n","    for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n","        model.eval()\n","        batch = tuple(t.to(\"cuda\") for t in batch)\n","\n","        with torch.no_grad():\n","            inputs = {\"input_ids\": batch[0], \"attention_mask\": batch[1], \"labels\": batch[3]}\n","            outputs = model(**inputs)\n","            tmp_eval_loss, logits = outputs[:2]\n","\n","            eval_loss += tmp_eval_loss.mean().item()\n","        nb_eval_steps += 1\n","        if preds is None:\n","            preds = logits.detach().cpu().numpy()\n","            out_label_ids = inputs[\"labels\"].detach().cpu().numpy()\n","        else:\n","            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n","            out_label_ids = np.append(out_label_ids, inputs[\"labels\"].detach().cpu().numpy(), axis=0)\n","\n","    eval_loss = eval_loss / nb_eval_steps\n","\n","    preds = np.argmax(preds, axis=1)\n","\n","    results = simple_accuracy(preds, out_label_ids)\n","\n","\n","\n","\n","    return results,eval_loss"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FqTvHnBs-jiU","colab_type":"code","colab":{}},"source":["# 3.生成评估数据\n","val_features = convert_examples_to_features(val_InputExamples, \n","                                            tokenizer, \n","                                            label_list=my_label_list, \n","                                            output_mode=\"classification\",  \n","                                            max_length=MAX_SEQ_LENGTH )\n","\n","\n","val_input_ids = torch.tensor([f.input_ids for f in val_features], dtype=torch.long)\n","val_attention_mask = torch.tensor([f.attention_mask for f in val_features], dtype=torch.long)\n","val_token_type_ids = torch.tensor([f.token_type_ids for f in val_features], dtype=torch.long)\n","val_the_labels = torch.tensor([f.label for f in val_features], dtype=torch.long)\n","\n","\n","eval_dataset = TensorDataset(val_input_ids, val_attention_mask, val_token_type_ids, val_the_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"bHwf4hX--akQ","colab_type":"code","outputId":"5623c80f-515d-4c66-e7bb-8b5a6f0025f3","executionInfo":{"status":"error","timestamp":1586066503472,"user_tz":-480,"elapsed":970,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":202}},"source":["# 4. 评估结果\n","results,eval_loss = evaluate(model, tokenizer, eval_dataset)\n","print()\n","print(\"Accuracy: \",results, \"Loss: \",eval_loss)"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-a1475d0bf1d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0meval_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Loss: \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0meval_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'evaluate' is not defined"]}]},{"cell_type":"markdown","metadata":{"id":"2WA12iDdBxsm","colab_type":"text"},"source":["### 这里是LSTM开始, 首先获得embedding, 并把embedding和label组合起来\n","### 获得df_train, df_val, df_test\n","### (13713, 2), (2057, 2), (1372, 2)"]},{"cell_type":"code","metadata":{"id":"Cyv7LvPDB6f8","colab_type":"code","outputId":"fb9e427e-7146-46cc-9cb5-3a7f3937c454","executionInfo":{"status":"ok","timestamp":1586235594646,"user_tz":-480,"elapsed":32744,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["args_eval={\"model_name_or_path\": \"/content/drive/My Drive/classification_models_1/\",\n","    \"config_name\": \"/content/drive/My Drive/classification_models_1/\",\n","    \"tokenizer_name\": \"/content/drive/My Drive/classification_models_1/\",\n","      }\n","\n","config_class, tokenizer_class = MODEL_CLASSES[\"bert\"]\n","model_class=BertForClassification\n","\n","\n","config = config_class.from_pretrained(\n","    args_eval[\"config_name\"],\n","    finetuning_task=\"\", \n","    cache_dir=None,\n",")\n","tokenizer = tokenizer_class.from_pretrained(\n","    args_eval[\"tokenizer_name\"],\n","    do_lower_case=True,\n","    cache_dir=None,\n",")\n","model = model_class.from_pretrained(\n","    args_eval[\"model_name_or_path\"],\n","    from_tf=bool(\".ckpt\" in args_eval[\"model_name_or_path\"]),\n","    config=config,\n","    cache_dir=None,\n",")\n","\n","\n","model.to(\"cuda\")"],"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["BertForClassification(\n","  (bert): BertModel(\n","    (embeddings): BertEmbeddings(\n","      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (token_type_embeddings): Embedding(2, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (encoder): BertEncoder(\n","      (layer): ModuleList(\n","        (0): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (1): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (2): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (3): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (4): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (5): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (6): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (7): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (8): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (9): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (10): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","        (11): BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (pooler): BertPooler(\n","      (dense): Linear(in_features=768, out_features=768, bias=True)\n","      (activation): Tanh()\n","    )\n","  )\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (classifier): Linear(in_features=768, out_features=10, bias=True)\n",")"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"fAYinf7ZhOb9","colab_type":"code","colab":{}},"source":["train_features = convert_examples_to_features(train_InputExamples,\n","                                              tokenizer, \n","                                              label_list=my_label_list, \n","                                              output_mode=\"classification\", \n","                                              max_length=MAX_SEQ_LENGTH )\n","\n","val_features = convert_examples_to_features(val_InputExamples, \n","                                            tokenizer, \n","                                            label_list=my_label_list, \n","                                            output_mode=\"classification\",  \n","                                            max_length=MAX_SEQ_LENGTH )"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"hUDJXWBIZVEU","colab":{}},"source":["train_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)\n","train_attention_mask = torch.tensor([f.attention_mask for f in train_features], dtype=torch.long)\n","train_token_type_ids = torch.tensor([f.token_type_ids for f in train_features], dtype=torch.long)\n","train_the_labels = torch.tensor([f.label for f in train_features], dtype=torch.long)\n","\n","# 这里一步每个东西都是要传到BERT模型的forward里面的, 要传哪些自己准备好\n","train_dataset = TensorDataset(train_input_ids, train_attention_mask, train_token_type_ids, train_the_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","id":"M14vSL2HZTOC","colab":{}},"source":["val_input_ids = torch.tensor([f.input_ids for f in val_features], dtype=torch.long)\n","val_attention_mask = torch.tensor([f.attention_mask for f in val_features], dtype=torch.long)\n","val_token_type_ids = torch.tensor([f.token_type_ids for f in val_features], dtype=torch.long)\n","val_the_labels = torch.tensor([f.label for f in val_features], dtype=torch.long)\n","\n","# 这里一步每个东西都是要传到BERT模型的forward里面的, 要传哪些自己准备好\n","val_dataset = TensorDataset(val_input_ids, val_attention_mask, val_token_type_ids, val_the_labels)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BSwblOF6CFWC","colab_type":"code","colab":{}},"source":["def get_prediction(model, tokenizer, dataset):\n","\n","    logger.info(\"***** Running prediction  *****\")\n","    logger.info(\"  Num examples = %d\", len(dataset))\n","    logger.info(\"  Batch size = %d\", 16)\n","\n","    pooled_outputs = None\n","\n","    sampler =SequentialSampler(dataset)\n","    dataloader = DataLoader(dataset, sampler=sampler, batch_size=32)\n","\n","    for batch in tqdm(dataloader, desc=\"Evaluating\"):\n","        model.eval()\n","        batch = tuple(t.to(\"cuda\") for t in batch)\n","\n","        with torch.no_grad():\n","            inputs = {\"input_ids\": batch[0], \"attention_mask\": batch[1], \"labels\": batch[3]}\n","            outputs = model(**inputs)\n","            pooled_output = outputs[2]\n","\n","            if pooled_outputs is None:\n","                pooled_outputs = pooled_output.detach().cpu().numpy()\n","            else:\n","                pooled_outputs = np.append(pooled_outputs, pooled_output.detach().cpu().numpy(), axis=0)\n","\n","    return pooled_outputs"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"9c1e4396-3ea8-4eb8-bb74-e1041ce65c1e","executionInfo":{"status":"ok","timestamp":1586236141619,"user_tz":-480,"elapsed":389689,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"id":"M6YGCJyfZS4L","colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["train_pooled_outputs = get_prediction(model, tokenizer, train_dataset)\n","train_pooled_outputs.shape"],"execution_count":32,"outputs":[{"output_type":"stream","text":["Evaluating: 100%|██████████| 988/988 [06:28<00:00,  2.55it/s]\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["(31600, 768)"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"66711602-02c4-4151-d08d-dbb137164113","executionInfo":{"status":"ok","timestamp":1586236245820,"user_tz":-480,"elapsed":100686,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"id":"qU6dPculZQk6","colab":{"base_uri":"https://localhost:8080/","height":51}},"source":["val_pooled_outputs = get_prediction(model, tokenizer, val_dataset)\n","val_pooled_outputs.shape"],"execution_count":33,"outputs":[{"output_type":"stream","text":["Evaluating: 100%|██████████| 251/251 [01:40<00:00,  2.51it/s]\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["(8025, 768)"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"fb3DVyDQJ4J2","colab_type":"code","outputId":"da9bb0a8-a101-441e-dfdf-ffa6a9be3a6a","executionInfo":{"status":"ok","timestamp":1586236249172,"user_tz":-480,"elapsed":1402,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["aux = -1\n","len_l = 0\n","train_x = {}\n","for l, emb in zip(index_l, train_pooled_outputs):\n","    if l in train_x.keys():\n","        train_x[l]  =np.vstack([train_x[l], emb])\n","    else:\n","        train_x[l] = [emb]\n","\n","len(train_x.keys())"],"execution_count":34,"outputs":[{"output_type":"execute_result","data":{"text/plain":["13713"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"code","metadata":{"id":"TABOLdIXlKn5","colab_type":"code","outputId":"a4ebb95b-902b-4b77-dd4b-3b306fb6e420","executionInfo":{"status":"ok","timestamp":1586236254742,"user_tz":-480,"elapsed":3907,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["train_l_final = []\n","label_l_final = []\n","for k in train_x.keys():\n","    train_l_final.append(train_x[k])\n","    label_l_final.append(train.loc[k]['label'])\n","\n","df_train = pd.DataFrame({'emb': train_l_final, 'label': label_l_final, })\n","df_train.head()"],"execution_count":35,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>emb</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[[-0.66598725, 0.016773714, 0.9182689, -0.2799...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[[-0.80831516, 0.7271213, 0.8557929, -0.756151...</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[[0.32187176, 0.026258362, -0.37871468, -0.638...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[[-0.83225095, 0.69810855, 0.81367195, -0.3654...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[[-0.78828704, 0.755584, 0.76623535, -0.145819...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 emb  label\n","0  [[-0.66598725, 0.016773714, 0.9182689, -0.2799...      0\n","1  [[-0.80831516, 0.7271213, 0.8557929, -0.756151...      4\n","2  [[0.32187176, 0.026258362, -0.37871468, -0.638...      1\n","3  [[-0.83225095, 0.69810855, 0.81367195, -0.3654...      6\n","4  [[-0.78828704, 0.755584, 0.76623535, -0.145819...      6"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"Mp79AXw1ldUU","colab_type":"code","outputId":"85d53e36-7c91-4133-b905-3913ae833608","executionInfo":{"status":"ok","timestamp":1586236259180,"user_tz":-480,"elapsed":1965,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"source":["aux = -1\n","len_l = 0\n","val_x = {}\n","\n","for l, emb in zip(val_index_l, val_pooled_outputs):\n","    if l in val_x.keys():\n","        val_x[l]  =np.vstack([val_x[l], emb])\n","    else:\n","        val_x[l] = [emb]\n","\n","\n","val_l_final = []\n","vlabel_l_final = []\n","for k in val_x.keys():\n","    val_l_final.append(val_x[k])\n","    vlabel_l_final.append(val.loc[k]['label'])\n","\n","df_val = pd.DataFrame({'emb': val_l_final, 'label': vlabel_l_final})\n","df_val.head()"],"execution_count":36,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>emb</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[[-0.79879385, -0.4457972, -0.49054667, 0.6961...</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[[-0.92712766, 0.776308, 0.9671023, -0.4620155...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>[[0.31076232, -0.4100751, -0.9711022, 0.771664...</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>[[-0.6904813, 0.6151365, 0.64016944, -0.731770...</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>[[-0.8227867, 0.7430515, 0.81126, -0.46570382,...</td>\n","      <td>6</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 emb  label\n","0  [[-0.79879385, -0.4457972, -0.49054667, 0.6961...      2\n","1  [[-0.92712766, 0.776308, 0.9671023, -0.4620155...      6\n","2  [[0.31076232, -0.4100751, -0.9711022, 0.771664...      1\n","3  [[-0.6904813, 0.6151365, 0.64016944, -0.731770...      6\n","4  [[-0.8227867, 0.7430515, 0.81126, -0.46570382,...      6"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"code","metadata":{"id":"SF4VuaZ3mEvb","colab_type":"code","colab":{}},"source":["df_val, df_test = train_test_split(df_val, test_size=0.4, random_state=35)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YMQXBSeX89ne","colab_type":"code","outputId":"060ae168-622e-4c29-915f-775d94c6fe72","executionInfo":{"status":"ok","timestamp":1586236266342,"user_tz":-480,"elapsed":1153,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["df_train.shape, df_val.shape, df_test.shape"],"execution_count":38,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((13713, 2), (2057, 2), (1372, 2))"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"markdown","metadata":{"id":"c2tPtP3w82Y5","colab_type":"text"},"source":["### 生成iterator"]},{"cell_type":"code","metadata":{"id":"AjEi5Guyil8A","colab_type":"code","colab":{}},"source":["def train_generator(df):\n","    num_sequences = len(df['emb'].to_list())\n","    batch_size = 3\n","    batches_per_epoch =  4571\n","    assert batch_size * batches_per_epoch == num_sequences\n","    num_features= 768\n","\n","\n","\n","    x_list= df['emb'].to_list()\n","    y_list =  df.label.to_list()\n","    # Generate batches\n","    while True:\n","        for b in range(batches_per_epoch):\n","            longest_index = (b + 1) * batch_size - 1\n","            timesteps = len(max(df['emb'].to_list()[:(b + 1) * batch_size][-batch_size:], key=len))\n","            x_train = np.full((batch_size, timesteps, num_features), -99.)\n","            y_train = np.zeros((batch_size,  1))\n","            for i in range(batch_size):\n","                li = b * batch_size + i\n","                x_train[i, 0:len(x_list[li]), :] = x_list[li]\n","                y_train[i] = y_list[li]\n","            yield torch.tensor(x_train), torch.tensor(y_train,dtype=int)\n","            # keras版本请使用这句,不需要转torch tensor\n","            # yield x_train, y_train"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vMSrj7JoASRH","colab_type":"code","outputId":"9716a7c1-7928-4450-e458-b744081353a9","executionInfo":{"status":"ok","timestamp":1586180159296,"user_tz":-480,"elapsed":927,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":391}},"source":["kk= iter(train_generator(df_train))\n","bbb = next(kk)\n","print(bbb)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(tensor([[[-7.3258e-01, -3.3298e-01, -9.2938e-02,  ...,  1.6642e-02,\n","           9.0074e-01,  8.0857e-01],\n","         [-9.9000e+01, -9.9000e+01, -9.9000e+01,  ..., -9.9000e+01,\n","          -9.9000e+01, -9.9000e+01],\n","         [-9.9000e+01, -9.9000e+01, -9.9000e+01,  ..., -9.9000e+01,\n","          -9.9000e+01, -9.9000e+01]],\n","\n","        [[ 1.4018e-01, -5.8390e-01, -6.4310e-01,  ...,  8.4645e-01,\n","           7.6865e-01,  5.3188e-01],\n","         [ 5.7051e-02, -3.9838e-01, -4.9730e-01,  ...,  9.2459e-01,\n","           9.0167e-01,  7.3650e-01],\n","         [-9.9000e+01, -9.9000e+01, -9.9000e+01,  ..., -9.9000e+01,\n","          -9.9000e+01, -9.9000e+01]],\n","\n","        [[-2.4707e-01, -5.6092e-01, -4.3814e-02,  ...,  3.7149e-01,\n","           9.1072e-01,  8.1561e-01],\n","         [-1.5477e-01, -2.0997e-01, -9.2006e-01,  ..., -6.1910e-01,\n","           7.1046e-01,  3.4675e-02],\n","         [ 4.9451e-01, -6.5452e-01, -3.3381e-01,  ..., -8.0707e-01,\n","           7.3640e-03, -7.9465e-01]]], dtype=torch.float64), tensor([[2],\n","        [4],\n","        [8]]))\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NiO8gh70ltaB","colab_type":"code","colab":{}},"source":["def val_generator(df):\n","    num_sequences_val = len(df['emb'].to_list())\n","    batch_size_val = 11\n","    batches_per_epoch_val = 187\n","    assert batch_size_val * batches_per_epoch_val == num_sequences_val\n","    num_features= 768\n","\n","\n","    x_list= df['emb'].to_list()\n","    y_list =  df.label.to_list()\n","    # Generate batches\n","    while True:\n","        for b in range(batches_per_epoch_val):\n","            longest_index = (b + 1) * batch_size_val - 1\n","            timesteps = len(max(df['emb'].to_list()[:(b + 1) * batch_size_val][-31:], key=len))\n","            # print(len(df_train['emb'].to_list()[:b+batch_size][-7:]))\n","            x_train = np.full((batch_size_val, timesteps, num_features), -99.)\n","            y_train = np.zeros((batch_size_val,  1))\n","            for i in range(batch_size_val):\n","                li = b * batch_size_val + i\n","                # print(\"li\", li)\n","                # print(x_train[i, 0:len(x_list[li]), :].shape, len(x_list[li]))\n","                x_train[i, 0:len(x_list[li]), :] = x_list[li]\n","                y_train[i] = y_list[li]\n","            yield torch.tensor(x_train), torch.tensor(y_train,dtype=int)\n","            # keras版本请使用这句,不需要转torch tensor\n","            # yield x_train, y_train"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AZoyzyD_9vLg","colab_type":"text"},"source":["### Keras版本搭建"]},{"cell_type":"code","metadata":{"id":"616B6hk8pf4Y","colab_type":"code","outputId":"fcfb2350-b9eb-4730-a060-a4feb6cac90a","executionInfo":{"status":"ok","timestamp":1586096079821,"user_tz":-480,"elapsed":1720,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":360}},"source":["import tensorflow as tf\n","\n","\n","text_input = tf.keras.Input(shape=(None,768,), dtype='float32', name='text')\n","\n","# keras.layers.Masking(mask_value=0.0)是用于对值为指定值的位置进行掩蔽的操作，以忽略对应的timestep。\n","l_mask = tf.keras.layers.Masking(mask_value=-99.)(text_input) \n","\n","# Which we encoded in a single vector via a LSTM\n","encoded_text = tf.keras.layers.LSTM(100,)(l_mask)\n","out_dense = tf.keras.layers.Dense(30, activation='relu')(encoded_text)\n","# And we add a softmax classifier on top\n","out = tf.keras.layers.Dense(10, activation='softmax')(out_dense)\n","# At model instantiation, we specify the input and the output:\n","model = tf.keras.Model(text_input, out)\n","model.compile(optimizer='adam',\n","              loss='sparse_categorical_crossentropy',\n","              metrics=['acc'])\n","model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n","Model: \"model\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","text (InputLayer)            [(None, None, 768)]       0         \n","_________________________________________________________________\n","masking_2 (Masking)          (None, None, 768)         0         \n","_________________________________________________________________\n","lstm_2 (LSTM)                (None, 100)               347600    \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 30)                3030      \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 10)                310       \n","=================================================================\n","Total params: 350,940\n","Trainable params: 350,940\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"_eXq37MJsnj4","colab_type":"code","colab":{}},"source":["call_reduce = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_acc', factor=0.95, patience=3, verbose=2,\n","                                mode='auto', min_delta=0.01, cooldown=0, min_lr=0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dt5GZN-ns87e","colab_type":"code","outputId":"1027651b-0099-4e51-fdd3-53a3eb1a3281","executionInfo":{"status":"ok","timestamp":1586096519276,"user_tz":-480,"elapsed":278218,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":547}},"source":["model.fit_generator(train_generator(df_train), steps_per_epoch=batches_per_epoch, epochs=10,\n","                    validation_data=val_generator(df_val), validation_steps=batches_per_epoch_val, callbacks =[call_reduce] )"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From <ipython-input-76-9a0a8dbff73c>:2: Model.fit_generator (from tensorflow.python.keras.engine.training_v1) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use Model.fit, which supports generators.\n","Epoch 1/10\n","4571/4571 [==============================] - 27s 6ms/step - loss: 0.2116 - acc: 0.9471 - val_loss: 0.2097 - val_acc: 0.9494 - lr: 0.0010\n","Epoch 2/10\n","4571/4571 [==============================] - 27s 6ms/step - loss: 0.1836 - acc: 0.9529 - val_loss: 0.2080 - val_acc: 0.9470 - lr: 0.0010\n","Epoch 3/10\n","4571/4571 [==============================] - 27s 6ms/step - loss: 0.1757 - acc: 0.9542 - val_loss: 0.2091 - val_acc: 0.9490 - lr: 0.0010\n","Epoch 4/10\n","4569/4571 [============================>.] - ETA: 0s - loss: 0.1682 - acc: 0.9556\n","Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0009500000451225787.\n","4571/4571 [==============================] - 27s 6ms/step - loss: 0.1681 - acc: 0.9556 - val_loss: 0.2061 - val_acc: 0.9514 - lr: 0.0010\n","Epoch 5/10\n","4571/4571 [==============================] - 28s 6ms/step - loss: 0.1630 - acc: 0.9566 - val_loss: 0.2047 - val_acc: 0.9490 - lr: 9.5000e-04\n","Epoch 6/10\n","4571/4571 [==============================] - 27s 6ms/step - loss: 0.1512 - acc: 0.9590 - val_loss: 0.2041 - val_acc: 0.9490 - lr: 9.5000e-04\n","Epoch 7/10\n","4566/4571 [============================>.] - ETA: 0s - loss: 0.1470 - acc: 0.9604\n","Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.0009025000152178108.\n","4571/4571 [==============================] - 28s 6ms/step - loss: 0.1469 - acc: 0.9604 - val_loss: 0.2121 - val_acc: 0.9490 - lr: 9.5000e-04\n","Epoch 8/10\n","4571/4571 [==============================] - 28s 6ms/step - loss: 0.1412 - acc: 0.9618 - val_loss: 0.2075 - val_acc: 0.9499 - lr: 9.0250e-04\n","Epoch 9/10\n","4571/4571 [==============================] - 28s 6ms/step - loss: 0.1362 - acc: 0.9631 - val_loss: 0.2125 - val_acc: 0.9519 - lr: 9.0250e-04\n","Epoch 10/10\n","4569/4571 [============================>.] - ETA: 0s - loss: 0.1317 - acc: 0.9648\n","Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0008573750033974647.\n","4571/4571 [==============================] - 28s 6ms/step - loss: 0.1316 - acc: 0.9648 - val_loss: 0.2173 - val_acc: 0.9485 - lr: 9.0250e-04\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7f2f39a9d9b0>"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"L48cWceMuTI3","colab_type":"code","outputId":"05e4a0a3-103e-46e1-bf35-a9b5f921016d","executionInfo":{"status":"ok","timestamp":1586097478498,"user_tz":-480,"elapsed":2485,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["num_sequences_val = len(df_test['emb'].to_list())\n","batch_size_val = 4\n","batches_per_epoch_val = 343\n","assert batch_size_val * batches_per_epoch_val == num_sequences_val\n","num_features= 768\n","model.evaluate_generator(val_generator(df_test), steps= batches_per_epoch_val)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[0.23650717299089444, 0.94752187]"]},"metadata":{"tags":[]},"execution_count":82}]},{"cell_type":"markdown","metadata":{"id":"mYd-pZwS94kX","colab_type":"text"},"source":["### PyTorch版本搭建"]},{"cell_type":"code","metadata":{"id":"QIpvzzmUm-lU","colab_type":"code","colab":{}},"source":["class MyLSTM(nn.Module):\n","    def  __init__(self):\n","        super(MyLSTM, self).__init__()\n","        self.lstm = nn.LSTM(768, 100, batch_first=True)\n","        self.fc1 = nn.Linear(100,30)\n","        self.fc2 = nn.Linear(30,10)\n","\n","    def forward(self, inputs):\n","        output, (hidden,cell) = self.lstm(inputs)   # 1 * batch_size * 768\n","        hidden = hidden.squeeze(0)  # batch_size * 768\n","        hidden = F.relu(self.fc1(hidden))  # batch_size * 30\n","        hidden = F.softmax(self.fc2(hidden),dim = 1)  # batch_size * 10\n","        return hidden\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6mvzfMb4bxZq","colab_type":"code","colab":{}},"source":["def cal_accuracy(preds, labels): #计算准确率\n","    preds = preds.detach().cpu().numpy()\n","    labels = labels.detach().cpu().numpy()\n","    preds = np.argmax(preds, axis=1)\n","    return (preds == labels).mean()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-rFjMArvt9vc","colab_type":"code","colab":{}},"source":["def train_model(model,iterator, optimizer,loss_fn):   #训练函数\n","    epoch_loss=0.\n","    epoch_acc=0.\n","    total_len=0.\n","    model.train()\n","    \n","    #for embedding, label in tqdm(iterator, desc=\"Training\",disable=False):\n","    for idx, batch in enumerate(iterator):\n","        optimizer.zero_grad()\n","\n","        predictions = model(embedding.float())\n","\n","        loss=loss_fn(predictions,label.view(-1))\n","        acc=cal_accuracy(predictions, label)\n","        \n","        \n","        loss.backward()\n","        optimizer.step()\n","\n","        epoch_loss += loss.item() * len(label)\n","        epoch_acc += acc.item() * len(label)        \n","        total_len += len(label)\n","        \n","    return epoch_loss / total_len, epoch_acc / total_len  "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Nw78pYz0j6Dj","colab_type":"code","colab":{}},"source":["def evaluate_model(model, iterator, loss_fn):   # 评估函数\n","    epoch_loss = 0\n","    epoch_acc = 0\n","    total_len = 0\n","    \n","    model.eval()\n","\n","    \n","    with torch.no_grad():\n","        for embedding, label in iterator: \n","\n","            predictions = model(embedding.float())\n","            loss = loss_fn(predictions, label.view(-1))\n","            acc = cal_accuracy(predictions, label)\n","            \n","            \n","            epoch_loss += loss.item() * len(label)\n","            epoch_acc += acc.item() * len(label)\n","            total_len += len(label)\n","    model.train() #调回训练模式   \n","    \n","    return epoch_loss / total_len, epoch_acc / total_len"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"erCynIKPHJoO","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":168},"outputId":"526e8fd9-7619-48f8-9564-ee7526173dbf","executionInfo":{"status":"error","timestamp":1586241132748,"user_tz":-480,"elapsed":1027,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}}},"source":["print(df_train.emb)"],"execution_count":1,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-f6de533a58ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'df_train' is not defined"]}]},{"cell_type":"code","metadata":{"id":"Pbi6Y_iFEpWA","colab_type":"code","colab":{}},"source":["from torchtext.data import Iterator, BucketIterator\n","train_iter = Iterator(df_train, batch_size=8, device=\"cuda\", sort_key=lambda x: len(x), sort_within_batch=False, repeat=False)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"PzeVOdojFOeR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"950065bd-0d99-44b7-86ca-7fce4cab2675","executionInfo":{"status":"error","timestamp":1586237236393,"user_tz":-480,"elapsed":1484,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}}},"source":["for i in train_iter:\n","  print(i)"],"execution_count":59,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 8199","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-59-31b743a70b3a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtrain_iter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchtext/data/iterator.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m             \u001b[0;32mfor\u001b[0m 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129\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchtext/data/iterator.py\u001b[0m in \u001b[0;36mdata\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    103\u001b[0m             \u001b[0mxs\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 8199"]}]},{"cell_type":"code","metadata":{"id":"U42XS7iWjq-g","colab_type":"code","outputId":"53e45344-005b-453b-f920-ba2d724ccc36","executionInfo":{"status":"error","timestamp":1586236784829,"user_tz":-480,"elapsed":1121,"user":{"displayName":"たまものまえ","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgIvpb4WOBPZHWDEX5EMW9ZgFlTOlBbpFGYLwJy=s64","userId":"07646311357530751070"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["lstm_model = MyLSTM()\n","optimizer=optim.Adam(lstm_model.parameters()) \n","loss_fn = CrossEntropyLoss()\n","\n","N_EPOCHS = 5\n","best_valid_loss = float('inf') \n","\n","\n","#train_iterator = train_generator(df_train)\n","#val_iterator = val_generator(df_val)\n","\n","for epoch in range(N_EPOCHS):\n","\n","    \n","    train_loss, train_acc = train_model(lstm_model, train_iter, optimizer, loss_fn)\n","    \n","    valid_loss, valid_acc = evaluate_model(lstm_model, val_iter, loss_fn)\n","    \n","    \n","    if valid_loss < best_valid_loss: #保存DEV(验证集)上效果最好的模型\n","        best_valid_loss = valid_loss\n","        # torch.save(model_1.state_dict(), 'wordavg-model.pt')\n","    \n","    print('Epoch-'+str(epoch+1)+' :')\n","    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n","    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"],"execution_count":48,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 8800","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-48-2c2c38b9480a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m     \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mvalid_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlstm_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_iter\u001b[0m\u001b[0;34m,\u001b[0m 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10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torchtext/data/iterator.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminibatch\u001b[0m \u001b[0;32min\u001b[0m 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